U.S. patent application number 13/598339 was filed with the patent office on 2013-04-18 for apparatus and systems for event detection using probabilistic measures.
This patent application is currently assigned to FLINT HILLS SCIENTIFIC, L.L.C.. The applicant listed for this patent is Alexey Lyubushin, Ivan Osorio, Didier Sornette. Invention is credited to Alexey Lyubushin, Ivan Osorio, Didier Sornette.
Application Number | 20130096393 13/598339 |
Document ID | / |
Family ID | 47226396 |
Filed Date | 2013-04-18 |
United States Patent
Application |
20130096393 |
Kind Code |
A1 |
Osorio; Ivan ; et
al. |
April 18, 2013 |
APPARATUS AND SYSTEMS FOR EVENT DETECTION USING PROBABILISTIC
MEASURES
Abstract
Methods, systems, and apparatus for determining probabilistic
measures of seizure activity (PMSA) values based on a plurality of
seizure detection algorithms and/or body signals used as inputs by
the seizure detection algorithms. Use of the PMSA values to detect
seizure activity based on a consensus of the algorithms and/or body
signals, and/or warn, log, administer a therapy, or assess the
efficacy of a therapy.
Inventors: |
Osorio; Ivan; (Leawood,
KS) ; Lyubushin; Alexey; (Moscow, RU) ;
Sornette; Didier; (Zurich, CH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Osorio; Ivan
Lyubushin; Alexey
Sornette; Didier |
Leawood
Moscow
Zurich |
KS |
US
RU
CH |
|
|
Assignee: |
FLINT HILLS SCIENTIFIC,
L.L.C.
Lawrence
KS
|
Family ID: |
47226396 |
Appl. No.: |
13/598339 |
Filed: |
August 29, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13554367 |
Jul 20, 2012 |
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13598339 |
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13554694 |
Jul 20, 2012 |
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13554367 |
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13559116 |
Jul 26, 2012 |
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13554694 |
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61547567 |
Oct 14, 2011 |
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Current U.S.
Class: |
600/301 ;
600/300 |
Current CPC
Class: |
A61B 5/048 20130101;
A61B 5/726 20130101; A61B 5/4094 20130101 |
Class at
Publication: |
600/301 ;
600/300 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A method of detecting a seizure in a patient, comprising:
providing at least first and second seizure detection algorithms
for detecting seizure activity based upon at least one body signal;
and determining a probabilistic measure of seizure activity (PMSA)
value based upon the outputs of said at least first and second
seizure detection algorithms.
2. The method of claim 1, wherein the at least first and second
seizure detection algorithms are selected from an autoregression
algorithm, a wavelet transform maximum modulus (WTMM) algorithm, or
a short-term-average to long-term-average (STA/LTA) algorithm,
wherein the total number of seizure detection algorithms and body
signals is at least three.
3. The method of claim 2, wherein the at least one body signal
comprises at least one of a measurement of the patient's heart
activity, a measurement of the patient's respiratory activity, a
measurement of the patient's kinetic activity, a measurement of the
patient's brain electrical activity, a measurement of the patient's
brain chemical activity, a measurement of the patient's brain
temperature, a measurement of the patient's oxygen consumption, a
measurement of the patient's oxygen saturation, a measurement of an
endocrine activity of the patient, a measurement of a metabolic
activity of the patient, a measurement of an autonomic activity of
the patient, a measurement of a cognitive activity of the patient,
or a measurement of a tissue stress marker of the patient.
4. The method of claim 1, further comprising: selecting one or more
of: a number of seizure detection algorithms, said at least first
and second seizure detection algorithms, at least one parameter of
at least one of said first and second seizure detection algorithms,
a type of said PMSA, or at least one parameter of said PMSA, based
upon one or more of: a clinical application; a level of safety risk
associated with an activity; at least one of an age, physical
state, or mental state of the patient; a length of a window
available for warning; a degree of efficacy of therapy and of the
latency of its effect; a degree of seizure control; a degree of
circadian and ultradian fluctuations of said patient's seizure
activity; a performance of the detection method as a function of
the patient's sleep/wake cycle or vigilance level; a dependence of
the patient's seizure occurrence on at least one of a level of
consciousness, a circadian or ultradian rhythm, a level of
cognitive activity, or a level of physical activity; the site of
seizure origin; a seizure type or class; a proclivity for the
seizure to spread or to impair cognitive or motor functions; a
proclivity of the seizure to cause falls to the ground; a desired
sensitivity of detection of a seizure; a desired specificity of
detection of a seizure: a desired speed of detection of a seizure;
a time elapsed since a previous seizure; a previous seizure
severity; a probability of seizure occurrence; a likelihood of
seizure occurrence; an input provided by a user; or an input
provided by a machine.
5. The method of claim 4, wherein said selecting is one of a manual
selection or an automatic adaptive selection.
6. The method of claim 1, wherein said determining said PMSA value
comprises at least one of determining an average indicator function
by averaging said outputs of said at least first and second seizure
detection algorithms or determining a product indicator function by
multiplying said outputs of said at least first and second seizure
detection algorithms.
7. The method of claim 6, wherein said determining said PMSA value
comprises weighting one or more of said algorithm outputs.
8. The method of claim 1, further comprising comparing said PMSA
value to a PMSA threshold, and detecting a seizure event when said
PMSA value meets or exceeds the PMSA threshold.
9. The method of claim 8, wherein said PMSA threshold is
established based at least in part on at least one of a measurement
of the patient's heart activity, a measurement of the patient's
respiratory activity, a measurement of the patient's kinetic
activity, a measurement of the patient's brain electrical activity,
a measurement of the patient's oxygen consumption, a measurement of
the patient's oxygen saturation, a measurement of an endocrine
activity of the patient, a measurement of a metabolic activity of
the patient, a measurement of an autonomic activity of the patient,
a measurement of a cognitive activity of the patient, or a
measurement of a tissue stress marker of the patient.
10. The method of claim 8, wherein said PMSA threshold is
established based at least in part on at least one of a level of
safety risk associated with an activity; at least one of an age,
physical state, or mental state of the patient; a length of a
window available for warning; a degree of efficacy of therapy and
of its latency; a degree of seizure control; a degree of circadian
and ultradian fluctuations of said patient's seizure activity; a
performance of the detection method as a function of the patient's
sleep/wake cycle or vigilance level; a dependence of the patient's
seizure activity on at least one of a level of consciousness, a
level of cognitive activity, or a level of physical activity; the
site of seizure origin; a seizure type; a desired sensitivity of
detection of a seizure; a desired specificity of detection of a
seizure: a desired speed of detection of a seizure; an input
provided by a user; an input provided by a machine; a time elapsed
since a previous seizure; a previous seizure severity; a
probability of seizure occurrence or a likelihood of seizure
occurrence.
11. The method of claim 1, further comprising at least one of:
delivering a therapy for said seizure at a particular time, wherein
at least one of said therapy, said particular time, or both is
based upon said PMSA value; determining a second PMSA value in
response to said therapy; determining at least one of an efficacy
of said therapy or an occurrence of at least one side effect of
said therapy, wherein the efficacy is based on at least one of said
PMSA value or said second PMSA value; issuing a warning for said
seizure, wherein said warning is based upon said PMSA value; or
logging one or more values relating to said seizure, said
detection, said therapy, said efficacy, said side effect, or said
warning.
12. The method of claim 1, wherein said first and second seizure
detection algorithms operate in real-time or off-line.
13. A method of detecting a seizure, comprising: determining a
probabilistic measure of seizure activity (PMSA) value based upon
at least a first body signal received by a first sensor and a
second body signal received by a second sensor.
14. The method of claim 13, wherein each of the first body signal
and the second body signal comprises at least one of a measurement
of the patient's heart activity, a measurement of the patient's
respiratory activity, a measurement of the patient's kinetic
activity, a measurement of the patient's brain electrical activity,
a measurement of the patient's brain chemical activity, a
measurement of the patient's brain temperature, a measurement of
the patient's oxygen consumption, a measurement of the patient's
oxygen saturation, a measurement of an endocrine activity of the
patient, a measurement of a metabolic activity of the patient, a
measurement of an autonomic activity of the patient, a measurement
of a cognitive activity of the patient, or a measurement of a
tissue stress marker of the patient.
15. A method of detecting a seizure in a patient, comprising:
providing a wavelet transform maximum modulus-stepwise
approximation (WTMM-Sp) algorithm for detecting seizure activity
based upon at least one body signal; and determining a
probabilistic measure of seizure activity (PMSA) value based upon
said WTMM-Sp algorithm output.
16. A non-transitive, computer readable program storage device
comprising instructions that, when executed by a processor, perform
a method, comprising: using a first seizure algorithm for detecting
a seizure activity based upon a first body signal; using a second
seizure algorithm for detecting said seizure activity based upon a
second body signal; and determining a probabilistic measure of
seizure activity (PMSA) value based upon the outputs of said at
least first and second seizure detection algorithms.
17. The non-transitive, computer-readable storage device of claim
16, including data that when executed by a processor performs the
method of claim 16, wherein using said first seizure algorithm
comprises determining at least one of a seizure onset, seizure
termination, or an occurrence of a seizure from said at least one
body signal, and using said second seizure algorithm comprises
determining at least one of said seizure onset, seizure
termination, or said occurrence of a seizure from said at least one
body signal, and determining said PMSA value comprises determining
an indicator function value based upon at least said first and
second values by at least one of averaging said first and second
values or multiplying said first and second values; and said method
further comprising: receiving at least one body signal; assigning a
first value based upon said determination using said first seizure
algorithm that at least one of said seizure onset, seizure
termination, or said occurrence of a seizure has occurred;
assigning a second value based upon said determination using said
second seizure algorithm that at least one of said seizure onset,
seizure termination, or an occurrence of a seizure has occurred;
comparing said indicator function value to a threshold; and
determining that a seizure has occurred based upon a determination
that said indicator function value is above said threshold.
18. The non-transitive, computer-readable storage device of claim
16, including data that when executed by a processor performs the
method of claim 16, wherein the method further comprises
determining at least one of the duration, the intensity, or the
extent of spread of said seizure.
19. The non-transitive, computer-readable storage device of claim
17, including data that when executed by a processor performs the
method of claim 17, further comprising using a third algorithm to
determine at least one of said seizure onset, seizure termination,
or said occurrence of a seizure from said at least one body signal,
assigning a third value based upon said determination, and wherein
determining said PMSA value further comprises determining an
indicator function value based upon at least said third value.
20. The non-transitive, computer-readable storage device of claim
19, including data that when executed by a processor performs the
method of claim 19, wherein each of said first, second, and third
algorithms is selected from an autoregression algorithm, a wavelet
transform maximum modulus (WTMM) algorithm, or a short term
average/long term average (STA/LTA) algorithm.
21. The non-transitive, computer-readable storage device of claim
16, including data that when executed by a processor performs the
method of claim 16, wherein said threshold has the value (n-1)/n,
wherein n is the number of algorithms.
22. The non-transitive, computer-readable storage device of claim
21, including data that when executed by a processor performs the
method of claim 21, wherein n is 4 and said threshold has the value
0.75.
23. The non-transitive, computer-readable storage device of claim
16, including data that when executed by a processor performs the
method of claim 16, further comprising determining at least one of
a timing of delivery of therapy, a rate of delivery of a therapy, a
therapy type, a timing of sending a warning, a warning type, a
warning duration, or an efficacy index based upon a timing of said
indicator value.
Description
[0001] The present application claims priority from U.S.
provisional patent application Ser. No. 61/547,567, filed on Oct.
14, 2011, and is a continuation-in-part of each of prior-filed,
co-pending U.S. patent application Ser. Nos. 13/554,367, filed Jul.
20, 2012; 13/554,694, filed Jul. 20, 2012; and 13/559,116, filed
Jul. 26, 2012; all of which claim priority from provisional patent
application 61/547,567. Each application named in this paragraph is
hereby incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates generally to the field of
biological events detection. More particularly, it concerns
epileptic event detection by use of a plurality of algorithms
operating on a time series of patient body signal data.
[0004] 2. Description of Related Art
[0005] There have been various advancements in the area of seizure
detection, which remains a fairly subjective endeavor. The task of
automated detection of epileptic seizures is generally related to
and dependent on the definition of what is a seizure, definition
which to date is subjective and thus inconsistent within and among
experts. The lack of an objective and universal definition not only
complicates the task of validation and comparison of detection
algorithms, but possibly more importantly, the characterization of
the spatio-temporal behavior of seizures and of other dynamical
features required to formulate a comprehensive epilepsy theory.
[0006] The current state of automated seizure detection is, by
extension, a reflection of the power and limitations of visual
analysis, upon which it rests. The subjectivity intrinsic to expert
visual analysis of seizures and its incompleteness (it cannot
adequately quantify or estimate certain signal features, such as
power spectrum) confound the objectivity and reproducibility of
results of signal processing tools used for their automated
detection. What is more, several of the factors, that enter into
the determination of whether or not certain grapho-elements should
be classified as a seizure, are non-explicit ("gestalt-based") and
thus difficult to articulate, formalize and program into
algorithms.
[0007] Most, if not all, existing seizure detection algorithms are
structured to operate as expert electroencephalographers. Thus,
seizure detection algorithms that apply expert-based rules are at
once useful and deficient; useful as they are based on a certain
fund of irreplaceable clinical knowledge and deficient as human
analysis biases propagate into their architecture. These cognitive
biases which pervade human decision processes and which have been
the subject of formal inquiry are rooted in common practice
behaviors such as: a) The tendency to rely too heavily on one
feature when making decisions (e.g., if onset is not sudden, it is
unlikely to be a seizure because these are paroxysmal events); b)
To declare objects as equal if they have the same external
properties (e.g., this is a seizure because it is just as
rhythmical as those we score as seizures) or c) Classify phenomena
by relying on the ease with which associations come to mind (e.g.,
this pattern looks just like the seizures we reviewed
yesterday).
[0008] Seizure detection algorithms' discrepant results make
attainment of a unitary or universal seizure definition ostensibly
difficult; the notion that expert cognitive biases are the main if
not only obstacle on the path to "objectivity" is rendered tenuous
by certain results. These divergences in objective and reproducible
results may be attributable in part, but not solely, to the
distinctiveness in the architecture and parameters of each
algorithm. The fractal or multi-fractal structures of seizures
accounts at least in part for the differences in results and draws
attention to the so-called "Richardson effect". Richardson
demonstrated that the length of borders between countries (a
natural fractal) is a function of the size of the measurement tool,
increasing without limit as the tool's size is reduced. Mandelbrot,
in his seminal contribution "How long is the coast of Britain,"
stressed the complexities inherent to the Richardson effect, due to
the dependency of particular measurements on the scale of the tool
used to perform them. Although defining seizures as a function of a
detection tool would be acceptable, this approach may be
impracticable when comparisons between, for example, clinical
trials or algorithms are warranted. Another strategy to bring
unification of definitions is to universally adopt the use of one
method, but this would be to the detriment of knowledge mining from
seizure-time series and by extension to clinical epileptology.
[0009] To date, performance comparisons among myriad existing
algorithms have not been performed due to lack of a common and
adequate database, a limitation that this invention addresses.
However, if and when undertaken, said "comparisons" would be
largely unwarranted and have meager, if any, clinical
value/translatability, given that no universally accepted
definition of what is a "seizure" has been crafted. The process of
evaluation of seizure detection algorithms is plagued with
cognitive biases and other confounding intricacies that impede
achievement of consensus and in certain cases even of majority
agreement. Performance assessment of these seizure detection
algorithms relies entirely on expert visual analysis, which
provides the benchmarks (seizure onset and end times) from which
key metrics (detection latency in reference to electrographic and
clinical onset time ("speed of detection"), sensitivity,
specificity and positive predictive value) are derived, the effects
of cognitive biases propagate beyond the seizure/non-seizure
question into other aspects of the effectiveness of a particular
seizure detection algorithm.
SUMMARY OF THE INVENTION
[0010] In one embodiment, the present disclosure provides a method
of detecting a seizure in a patient, comprising: providing at least
first and second seizure detection algorithms for detecting seizure
activity based upon at least one body signal; and determining a
probabilistic measure of seizure activity (PMSA) value based upon
the outputs of said at least first and second seizure detection
algorithms.
[0011] In one embodiment, the present disclosure provides a method
of detecting a seizure, comprising: determining a probabilistic
measure of seizure activity (PMSA) value based upon at least a
first body signal received by a first sensor and a second body
signal received by a second sensor.
[0012] In one embodiment, the present disclosure provides a method
of detecting a seizure in a patient, comprising: providing a
wavelet transform maximum modulus-stepwise approximation (WTMM-Sp)
algorithm for detecting seizure activity based upon at least one
body signal; and determining a probabilistic measure of seizure
activity (PMSA) value based upon said WTMM-Sp algorithm output.
[0013] In one embodiment, the present disclosure provides a method,
comprising: using a first seizure algorithm for detecting a seizure
activity based upon a first body signal; using a second seizure
algorithm for detecting said seizure activity based upon a second
body signal; and determining a probabilistic measure of seizure
activity (PMSA) value based upon the outputs of said at least first
and second seizure detection algorithms.
[0014] In one embodiment, the present disclosure provides a medical
device comprising one or more elements configured to implement one
or more steps of a method referred to above.
[0015] In one embodiment, the present disclosure provides a
non-transitive, computer readable program storage device comprising
instructions that, when executed by a processor, perform a method
referred to above.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The invention may be understood by reference to the
following description taken in conjunction with the accompanying
drawings, in which like reference numerals identify like elements,
and in which:
[0017] FIG. 1 illustrates a medical device system for detecting and
classifying seizure events related to epilepsy from sensed body
data processed to extract features indicative of aspects of the
patient's epilepsy condition;
[0018] FIG. 2 provides a schematic representation of a medical
device, in accordance with one aspect of the present
disclosure;
[0019] FIG. 3 provides a schematic representation of a number of
data acquisition units of a medical device system, in accordance
with one aspect of the present disclosure;
[0020] FIG. 4 provides a schematic representation of a number of
data acquisition units of a medical device system, in accordance
with one aspect of the present disclosure;
[0021] FIG. 5 provides a schematic representation of a seizure
onset/termination unit of a medical device system, in accordance
with one aspect of the present disclosure;
[0022] FIG. 6a-d. Average Indicator Function value (AIF; grey
step-wise functions) of the probability that cortical activity
(black oscillations) is a seizure over a certain time interval;
[0023] FIG. 7. Plots of time scale-dependent correlations between
Haar wavelet coefficients of the indicator functions (IFs), between
pairs of detection methods and between each method and the averaged
indicator function (AIF).
[0024] FIG. 8a-d. Probability Measure of Seizure Activity estimated
using the Wavelet Transform Maximum Modulus--Stepwise
Approximations.
[0025] FIG. 9. Graphic of time scale-dependent correlations between
PMSA-AIF and PMSA-SA after smoothing of their step-wise functions
with Haar wavelets.
[0026] FIG. 10. Graphics of specificity functions for each method
as a function of time with respect to the Validated algorithms's
time of seizure detection. Upper left panel: Auto-regressive model
vs. Validated algorithm; Upper right panel: Short/Long Term Average
Method vs. Validated algorithm; Lower left panel: Wavelet transform
Maximum Modulus vs. Validated algorithm; Lower right panel: Product
Index Function vs. Validated algorithm.
[0027] FIG. 11a-d. Plots of the decimal logarithm of the dependence
of seizure energy on seizure duration (minimum duration: 2 sec.).
Seizure is defined as the product of the standard deviation of the
differentiated the ECoG and seizure duration (in sec.). Upper left
plot: Validated algorithm detections; Right upper plot: Short/Long
Term Average detections; Left lower plot: Auto-regressive model
detections; Right lower plot: Wavelet-Transform Maximum Modulus
detections.
[0028] FIG. 12. Empirical "tail" of the conditional probability
distribution functions for: (a) Seizure durations (minimum
duration: 2 sec); (b) the logarithm of seizure energy as estimated
with the four different methods (Validated: Red; Short/Long Term
Average: Blue, Auto-regressive model: Green; Wavelet Transform
Maximum Modulus: Black).
[0029] FIG. 13. ECoG before (upper panel) and after differentiation
(lower panel).
[0030] FIG. 14. Temporal evolution of the decimal logarithm of the
power spectrum of differentiated ECoG (as shown in FIG. 13, bottom
panel) estimated in 5 s moving windows.
[0031] FIG. 15 provides a flowchart depiction of a method, in
accordance with one aspect of the present disclosure;
[0032] FIG. 16 provides a flowchart depiction of a method, in
accordance with one aspect of the present disclosure;
[0033] FIG. 17 provides a flowchart depiction of a method, in
accordance with one aspect of the present disclosure; and
[0034] FIG. 18 provides a flowchart depiction of a method, in
accordance with one aspect of the present disclosure.
DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
[0035] In one aspect, the present disclosure provides several new
seizure detection algorithms that may be applied to one or more
streams of body data. Some of these algorithms rely principally on
power variance for detection of seizures, while others rely mainly
on power spectral shape.
[0036] In another aspect, the present disclosure exploits the
simultaneous application of two or more seizure detection
algorithms to derive a probabilistic measure of seizure activity
(PMSA), which may be used to issue detections with a certain
probability based on multiple inputs to a probability function. The
multiple inputs may be outputs from one or more seizure detection
algorithms and/or one algorithm operating on two or more data
streams each relating to a body signal. These algorithms include,
but are not limited to, the seizure detection algorithms referred
to in the previous paragraph. Other detection algorithms may be
applied to various cerebral (e.g., chemical, thermal, optical) or
body signals such as, autonomic (e.g., cardio-vascular,
respiratory), metabolic (e.g., lactic acid, arterial pH, free
radicals, endocrine (e.g., prolactin, cortisol) as required by the
task at hand. This multi-algorithm, multi-modal, or multi-signal
approach provides comprehensive spatio-temporal information about
the dynamics/behavior of an event (epileptic seizures in the
preferred embodiment) by allowing determination of the degree or
extent of corporal impact (of a seizure) as well as the sequence
and severity of involvement, thus expanding the state of the art
that focuses only on the brain. The multi-signal approach is rooted
in the observations that seizures may also affect the
cardiovascular, respiratory, metabolic, endocrine, and/or
musculo-skeletal systems, and that acquiring and analyzing their
signals (instead of or in addition to cerebral ones) may serve to
validate probabilistically a detection.
[0037] The number and properties of algorithms applied to a body
signal, the number and type of body signals (e.g.,
cerebral/neurologic, autonomic, endocrine), the type of
probabilistic measure of seizure activity, (e.g., average or
product indication functions to be defined below), and their value
selected for issuing event detections depend on the history and
clinical status of the patient, the class, severity and frequency
of seizures, the activity the patient will be engaging in, or is
engaged at the time an event is presumptively detected, the extent
and rapidity of spread within the brain and to other organs, the
efficacy of therapies, and the time they take to reach their
target, in turn determine the "optimal" detection speed,
sensitivity, specificity required for the abatement of seizures
control and prevention of injuries. Real-time ("on the run")
automated seizure detection provides the only means through which
contingent warning to minimize risk of injury to patients, delivery
of a therapy for control of seizures, or logging of the date, time
of onset and termination and severity may be performed.
[0038] This disclosure: a) Draws attention to the intricacies
inherent to the pursuit of a universal seizure definition even when
powerful, well understood signal analysis methods are utilized to
this end; b) Identifies this aim as a multi-objective optimization
problem and discusses the advantages and disadvantages of adopting
or rejecting a unitary seizure definition; c) Introduces a
Probabilistic Measure of Seizure Activity to manage this thorny
issue.
[0039] Seizure detection belongs to a class of optimization
problems known as "multi-objective" due to the competing nature
between objectives; improvements in specificity of detection
invariably degrade sensitivity and vice-versa. Attempts to achieve
a universal seizure definition using objective, quantitative means,
are likely to be fraught with similar competing objectives, but
imaginative application of tools from the field of multi-objective
optimization, among others, are likely to make this objective more
tractable.
[0040] Achieving a unitary seizure definition would be difficult,
as consensus among epileptologists as to what grapho-elements are
classifiable as ictal, is rare. In the absence of a universal
definition, issuing seizure warnings for certain cases will be
problematic and unsafe. For example, if a patient with seizures
wishes to operate power equipment or a motor vehicle, the absence
of a universal agreement on when the patient has had a seizure may
preclude any viable way of ensuring, using seizure detection
algorithms, that the patient's seizures are under sufficient
control to allow such activities to occur. To manage the
difficulties of a consensus seizure definition, substantive gains
are feasible through steps entailing, for example, the application
of advanced signal analysis tools to ECoG, to hasten the
identification of properties/features that would lead to the
probabilistic discrimination of seizures from non-seizures with
worthwhile sensitivity and specificity for the task at hand.
However, to even have a modicum of success, such an approach should
not ignore the non-stationarity of seizures and, should strike some
sort of balance between supervised (human) and unsupervised
(machine-learning) approaches. The resulting multidimensional
parameter space, expected to be broad and intricate, may also
foster discovery of hypothesized (e.g. pre-ictal) brain
sub-states.
[0041] The challenges posed by the attempt to define seizures
unitarily using objective means (distinct from visual analysis) may
be partly related to their fractal properties and understood
through a simplistic analogy to the so-called "Richardson effect".
A revision of the time-honored subjective definition of seizures
may be warranted to further advance epileptology.
[0042] The present inventors propose a Probabilistic Measure of
Seizure Activity (PMSA) as one possible strategy for
characterization of the multi-fractal, non-stationary structure of
seizures, in an attempt to eschew the more substantive limitations
intrinsic to other alternatives.
[0043] The PMSA may make use of "indicator functions" (IFs) denoted
.chi.algo for each algorithm `algo.` Generally speaking, an IF
returns a binary result of 0 (no seizure) or 1 (seizure). The IFs
may then be used to prepare a function that quantifies the degree
of concordance between algorithms. In one embodiment, the PMSA may
make use of an Average Indicator Function (AIF). In one embodiment,
the AIF is defined as:
AIF(t)=(.chi..sub.Val(t)+.chi..sub.r.sub.2(t)+.chi..sub.STA/LTA(t)+.chi.-
.sub.WTMM(t))/4
[0044] The subscripts Val, r2, STA/LTA and WTMM refer to four
different algorithms, particular embodiments of which are described
herein and/or in other related applications. One or more of these
algorithms may be used to detected seizures from one or more body
data streams including, but not limited to, a brain activity (e.g.,
EEG) data stream, a cardiac (e.g., a heart beat) data stream, and a
kinetic (e.g., body movement as measured by an accelerometer) data
stream.
[0045] "Val" refers to an algorithm for seizure detection using
ECoG data that has been validated by experts without reaching a
universal consensus about its performance (e.g., false positive,
false negative and true positive detections). An "r2" algorithm may
also be referred to herein as an "r 2," "autoregression," or
"autoregressive" algorithm. A "STA/LTA" algorithm refers to an
algorithm characterized by the ratio of a Short-Term Average to a
Long-Term Average. A "WTMM" algorithm refers to a Wavelet Transform
Maximum Modulus algorithm.
[0046] For determination of an AIF from the foregoing formula, an
algorithm's IF equals 1 for time intervals (0.5 sec in this
application) "populated" by ictal activity and 0 by inter-ictal
activity. The IF's are used to generate four stepwise time
functions, one for each of: a) a 2ndorder auto-regressive model
(r2); b) the Wavelet Transform Maximum Modulus (WTMM); c) the ratio
of short-to-long term averages (STA/LTA) and d) a Validated
algorithm (Val). With these IFs, the AIF is computed (its values
may range between [0-1] with intermediate values of 0.25, 0.5 and
0.75 in this embodiment). (Intermediate AIF values are functions of
the number of algorithms applied to the signal. Since in this study
4 methods were used and the range of the indicator function is
[0-1], the intermediated values are [0.25, 0.5, 0.75]). These
values [0-1] are estimates of the probability of seizure occurrence
at any given time. In another embodiment, the values of each
algorithm's IF may be weighted differently, and a composite IF
(e.g., a Weighted Indicator Function or WIF) different from the AIF
may be computed.
[0047] Human Seizure Time Series/ECoG
[0048] Data obtained from one subject undergoing evaluation for
epilepsy surgery with intra-cranial electrodes was selected for
analyses as it had the largest number of clinical and subclinical
seizures in the University of Kansas Medical Center Epilepsy
Database. ECoG was collected in accordance with the Center's
surgical evaluation protocol and with the Human Subjects Committee
requirements, which include signing of a consent form by the
subject.
[0049] The ECoG was recorded using electrodes implanted into the
amygdala, pes hippocampus and body of hippocampus bilaterally
through the temporal neocortex and had a duration of 6.9 days
(142,923,853 samples; 239.75 Hz sampling rate).
[0050] Differentiation of the ECoG signals used in the analyses For
efficient analyses, ECoG signal differentiation was performed, so
as to minimize the non-stationarity present in them. If Z(t) is raw
ECoG, then its difference is X(t)=Z(t)-Z(t-1), where (t)
corresponds to a sample time increment. This linear operation is
exactly invertible and, unlike band-pass filtering or detrending,
does not suppress low frequency fluctuations, but decreases their
overall influence. FIG. 13 illustrates the effect of this operation
on raw ECoG. The differentiated ECoG is less non-stationary
(chiefly at low frequencies) than the undifferentiated one (x-axis:
time in sec.; y-axis: amplitude in microvolts). FIG. 14 shows a
time-frequency map of the evolution of the power spectra of
differentiated ECoG segments. The power spectra are estimated
within 5 sec moving windows of length. Six brief seizures appear as
marked power spectrum increases (red and specks of white) in the
10-100 Hz. band (x-axis: time in sec.; y-axis: frequency (Hz);
color scale to the right of main graph).
[0051] Seizure Detection Methods
[0052] The following signal analysis methods were applied to the
electrocorticogram (ECoG) to derive metrics for the discrimination
of seizure from non-seizure signals:
[0053] (i) An Auto-Regression (AR) model of the 2nd order, yielding
autoregression coefficients and the logarithm of residual
variance,
[0054] (ii) Estimates of the logarithm of the standard deviation
(SD) of differentiated ECoG using long chains of wavelet transform
modulus maxima (WTMM chains) based on the first derivative of a
Gaussian function .about.exp(-t.sup.2) as a continuous wavelet
kernel, and
[0055] (iii) The ratio of the "short time average" (STA) to the
"long time average" (LTA), widely used in seismology for precise
real-time earthquake detection. The spectral and dynamical
similarities between seizures and earthquakes provide the
motivation for application of this method to epileptology.
[0056] (iv) A validated seizure detection algorithm used as a
reference to better interpret the results of the novel proposed
ones and to cast light on the intricacies and challenges of
discriminating seizure from non-seizure signals even when using
objective, quantitative means.
[0057] The use of autoregression, WTMM, STA/LTA, and the validated
methods for detecting seizure from body signal data is described in
more detail in U.S. patent application Ser. Nos. 13/554,694, filed
Jul. 20, 2012, 13/554,367, filed Jul. 20, 2012, and 13/559,116,
filed on Jul. 26, 2012, all of which are incorporated herein by
reference.
[0058] Results
[0059] The dependencies of AIF values on the detection algorithm
applied to the ECoG are illustrated in FIG. 6A-D. The AIF value
(0-1) of this function is calculated based on the output of each of
the four detection algorithms used and reflects the probability
that grapho-elements are ictal in nature; the higher the AIF value,
the greater the probability that the detection is a seizure. AIF
values of 1 (the activity is detected by all algorithms as a
seizure) or 0 (none of the algorithms classifies the
grapho-elements as a seizure) pose no ambiguity, but as shown in
this study, are likely to be less prevalent than intermediate
values [0<AIF<1]. As shown in FIG. 6, the larger amplitude,
longer oscillations are the only ones to have an AIF value of 1,
indicative of "consensus" among all detection algorithms (x-axis:
time; y-axis: AIF values). By way of example, cortical activity may
be classified as a seizure if the AIF value is 0.75, having been
detected by the majority (3/4) of methods. In examples presented
herein, four different methods (r2, WTMM, STA/LTA, and Val) were
investigated, but this number may vary according to the task at
hand; for warning for the purpose of allowing operation of a motor
vehicle, application of a larger number of detection algorithms to
cortical signals and an AIF value of 1 would be desirable while,
for automated delivery of an innocuous, power inexpensive therapy,
less algorithms and much lower AIF values would be tolerable.
[0060] The cross-correlation between each pair of algorithm's IF
and their average function (AIF) were calculated; since each of
these is a step function (see FIG. 6), the Haar wavelet transform
was applied to them to facilitate visualization of their value
(y-axis) as a function of this wavelet's logarithmic time scale
(x-axis (FIG. 7)). In FIG. 7, r2, STA/LTA and WTMM act as labels
for both columns (label on top) and rows (label to the right of
each row), whereas Val designates only the column below it and AIF
the row to its left. FIG. 7 may be viewed as the lower half of a
square matrix; this triangle's vertices are: the top left-most plot
depicts the correlation between Val and r2, the bottom left-most
plot the correlation between Val and AIF and the bottom right-most
graph, that between WTMM and AIF; all other correlations lie within
these vertices (y-axes: Correlation values; x-axes: Logarithmic
time scale). The correlations (indicative of the concordance level)
between each IF pair and between each method's IF and the AIF,
increases monotonically, reaching a maximum between 20-30 s, after
which they decrease also monotonically (except for AIF vs. r2): The
WTMM and r.sup.2 methods have the highest correlations with AIF for
time scales exceeding 100 sec. Since estimating the probability
measure of seizure activity based on the AIF requires the output of
at least two detection algorithms, a simpler approach is to apply
only one, a Wavelet Transform Maximum Modulus-Stepwise
Approximation (WTMM-SAp).
[0061] Let U(.xi..sub.j) be a logarithm of the standard deviation
of differentiated ECoG computed within "small" adjacent time
windows of length L and .xi..sub.j the time moments corresponding
to right-hand ends of these windows. Thus, .xi..sub.j values are
given within the step L.delta.t, where .delta.t is an ECoG time
interval.
[0062] Let S.sub.U(.xi.|a.sub.*.sup.(j)) be a WTMM-SAp computed for
the dyadic sequence of m dimensionless scale thresholds:
a.sub.*.sup.(j)=a.sub.*.sup.(0)2.sup.(j-1), j=1, . . . , m (26)
and S.sub.U.sup.(a)(.xi.) be their mean value:
S U ( a ) ( .xi. ) = j = 1 m S U ( .xi. a * ( j ) ) / m ( 27 )
##EQU00001##
[0063] The averaged WTMM-SAp S.sub.U.sup.(a)(.xi.) may reveal
abrupt changes of U(.xi..sub.j) for different scales (the use of a
dyadic sequence (26) suppresses "outliers"). The background is
estimated by a simple average within a moving time window of the
radius of n discrete values of .xi.:
S _ U ( a ) ( .xi. j ) = k = - n n S U ( a ) ( .xi. j + k ) / ( 2 n
+ 1 ) ( 28 ) ##EQU00002##
[0064] Seizures correspond to positive peaks of
S.sub.U.sup.(a)(.xi.) above background S.sub.U.sup.(a)(.xi.). Thus,
the values:
.DELTA.S.sub.U.sup.(a)(.xi.)=max{0,S.sub.U.sup.(a)(.xi.)-
S.sub.U.sup.(a)(.xi.)}.gtoreq.0 (29)
[0065] are regarded as a Measure of Seizure Activity (MSA). In
order to make this measure probabilistic (PMSA), consider an
empirical probability distribution function:
F.sub..DELTA.S.sub.U.sub.(a)(X)=Pr{.DELTA.S.sub.U.sup.(a)(.xi.)<X}
(30)
and let Q.sub..DELTA.S.sub.U.sub.(a)(.gamma.) be the
.gamma.-quantile of the function (30), i.e. the root of the
equation:
F.sub..DELTA.S.sub.U.sub.(a)(Q)=1-.gamma., 0<.gamma.<1
(31)
[0066] The PMSA is defined by the formula:
.mu.(.xi.)=min{.DELTA.S.sub.U.sup.(a)(.xi.),Q.sub..DELTA.S.sub.U.sub.(a)-
(.gamma.)}/Q.sub..DELTA.S.sub.U.sub.(a)(.gamma.),
0.ltoreq..mu.(.xi.).ltoreq.1 (32)
[0067] It should be underlined that the PMSA (32) is defined within
sequences of "small" time intervals of length L.delta.t and
.xi.=.xi..sub.j are discrete time values, corresponding to
right-hand ends of these time windows.
[0068] The method of constructing a PMSA based on the WTMM-SAp
utilizes the following parameters whose values are shown in
parentheses:
[0069] The number L of adjacent samples for computing the logarithm
of the standard deviations U(.xi..sub.j) for differentiated ECoG
increments (L=240).
[0070] The values of a.sub.*.sup.(0) for setting the dyadic
sequence of WTMM scale thresholds in the formula (26)
(a.sub.*.sup.(0)=5, m=6, e.g., the following scale thresholds were
used: 5, 10, 20, 40, 80 and 160).
[0071] The number n of .xi..sub.j values for the radius of the
moving averaging in formula (28) (n=200, e.g., for L=240 and
1/.delta.t=239.75 Hz, the averaging length within formula (28)
equals 401 sec).
[0072] The probability level .gamma. for calculating a quantile in
formula (31) (.gamma.=0.01).
[0073] FIG. 8a-d represent PMSA estimates using WTMM-Sp performed
on the same data as used in the PMSA estimates of FIG. 6a-d. The
oscillations in black are cortical activity and the grey stepwise
function, the probability value they correspond to seizures
(x-axes: time; y-axes PMSA values). The results of the estimations
of PMSA using WTMM-SAp (FIG. 8) differ in one aspect (lower number
of events with probability 1) from those obtained with the
PMSA-AIF, given the dissimilarities between these two approaches,
but are alike in uncovering the dependencies of PMSA on seizure
duration: in general, the shorter the duration of a detection, the
larger the discordance between detection methods, a "trait" that
interestingly, is also shared by expert epileptologists.
Inter-algorithmic concordance as evidenced by the cross-correlation
values between PMSA-WTMM-SAp and PMSA-AIF (FIG. 9) grow
quasi-linearly (albeit non-monotonically) with the temporal length
of seizures. The correlation value increases as a function of time,
reaching a maximum value (0.73) at about 250 s, before decaying
steeply thereafter. Worthy of comment is the decay in
cross-correlation values for seizure exceeding a certain length for
both PMSA-AIF and PMSA-WTMM-SAp.
[0074] The crafting of, or "convergence" towards, a unitary seizure
definition would be epistemologically expensive and may
thwart/delay deeper understanding of the dynamics of ictiogenesis
and of the spatio-temporal behavior of seizures at relevant
time-scales. In the absence of a universal definition, substantive
gains are feasible through steps entailing, for example, the
application of advanced signals analyses tools to ECoG, to hasten
the identification of properties/features that would lead to the
probabilistic discrimination of seizures from non-seizures with
worthwhile sensitivity and specificity for the task at hand. Tools
such as those available through cluster analysis of
multidimensional vectors of relevant features would aid in the
pursuit of automated seizure detection and quantification. To even
have a modicum of success, this approach should not ignore the
non-stationarity of seizures and strike some sort of balance
between supervised (human) and unsupervised machine-learning)
approaches. The resulting multidimensional parameter space,
expected to be broad and intricate, may also foster discovery of
hypothesized (e.g. pre-ictal) brain sub-states.
[0075] The total number of detections, their duration and the
percent time spent in seizure over the time series total duration
(6.9 days) are presented in Table 1.
TABLE-US-00001 TABLE 1 Summary statistics obtained by applying four
different detection methods (Validated Algorithm; r.sup.2; STA/LTA;
WTMM) to a prolonged human seizure time-series. The minimum
duration of seizures was set at 2 s because such duration is the
minimum possible for the WTMM method with the parameter L = 240.
Validated algorithm r.sup.2 STA/LTA WTMM Total number of 3184 7029
16275 10795 seizures with duration .gtoreq.2 s. Mean duration, s.
3.8 23 4.3 18.6 Median 3.4 7 3.5 6 duration, s. % time spent in 2
27 12 34 seizure
[0076] The STA/LTA yielded the largest number of detections, but
only the third largest time spent in seizure, given the shortness
of median duration of detection compared to those computed by the
WTMM and r.sup.2 methods. The mean and median durations of
detections issued by the r.sup.2 method were the longest, but the
WTMM algorithm surpassed all others in duration of time spent in
seizure.
[0077] In order to better understand these differences, an
indicator function (IF) is applied to the results. IF equals 1 for
the duration of a seizure and 0 before its onset and after its
termination (0 corresponds to non-seizure intervals as identified
by each method). The calculation of the IF generates four-stepwise
time functions, one for each detection method: .chi..sub.Val(t),
.chi..sub.r.sub.2(t), .chi..sub.STA/LTA(t) and .chi..sub.WTMM(t).
Using this IF, two additional functions are computed over a 0.1 s
running window: a) The average indicator function (AIF):
AIF(t)=(.chi..sub.Val(t)+.chi..sub.r.sub.2(t)+.chi..sub.STA/LTA(t)+.chi.-
.sub.WTMM(t))/4 (23)
[0078] and b) The product of indicator functions (PIF):
PIF(t)=.chi..sub.Val(t).chi..sub.r.sub.2(t).chi..sub.STA/LTA(t).chi..sub-
.WTMM(t) (24)
[0079] The AIF's values may vary between [0-1] (and can take on any
intermediate value 0.25, 0.5, 0.75 in this application) whereas the
PIF values are either 0 or 1; a PIF=1 corresponds to an AIF=1 and a
PIF=0, to an AIF<1. Time intervals for which AIF=PIF=1
correspond to seizures detected by all methods. Typically, AIF
values are smaller than 1 (e.g., only one or two out of the four
methods recognize the activity as ictal in nature) at the onset and
termination of certain type of ECoG activity but frequently reach
1, sometime into the ictus as all methods "reach consensus". Table
2 provides further evidence that, at some point in time, the
majority of seizures detected by the validated algorithm are also
detected by the other three methods, with WTTM detecting the
largest number (97%) and STA/LTA the second largest (91.5%) number
of seizures. More specifically and by way of example, the value
0.971 in Table 2 means that the WTMM method detections encompass
97.1% of seizure time intervals detected with the validated method,
with the exception of 1.6 s. that correspond to the delay/lag
between them in detecting seizure onsets (see below for
details).
TABLE-US-00002 TABLE 2 Method Spe.sub.Method_Val(0) max .tau. Spe
Method_Val ( .tau. ) ##EQU00003## arg max .tau. Spe Method_Val (
.tau. ) ##EQU00004## r.sup.2 0.628 0.882 -1.1 s WTMM 0.823 0.971
-1.6 s STA/ LTA 0.911 0.915 -0.4 s Values of specificity of the
three novel methods calculated with respect to the validated method
and time lag (as defined in the text) at which the specificities
attain their largest values.
[0080] Time intervals for which the pairwise product
.chi..sub.Val(t).chi..sub.r.sub.2(t)=1 correspond to seizures
detected by both the validated algorithm and r.sup.2. Dividing the
number of time intervals when
.chi..sub.Val(t).chi..sub.r.sub.2(t)=1 by the number of intervals
when .chi..sub.Val(t)=1, yields the specificity of the r.sup.2
method with respect to the validated algorithm. Since the validated
algorithm has an inherent delay of 1 s (the median filter's
foreground window is 2 s) and an additional duration constraint of
0.84 s. is imposed before a detection is issued, its onset and end
times are "delayed" compared to those yielded by the other methods.
To account for this delay and make comparisons more meaningful, the
specificity of the r.sup.2 with respect to the validated algorithm
is re-calculated as a function of a time shift .tau.:
Spe r 2 _ Val ( .tau. ) = t ( .chi. r 2 ( t + .tau. ) .chi. Val ( t
) ) / t .chi. Val ( t ) ( 25 ) ##EQU00005##
[0081] The specificity functions for the two other methods
Spe.sub.WTMM.sub.--.sub.Val(.tau.) and
Spe.sub.STA/LTA.sub.--.sub.Val(.tau.) are identically computed and
their maximum value (dependent on .tau.) may be regarded as the
mean value of the time delay of one method's function with respect
to another for seizure onset and end times. From the results shown
in FIG. 10, it can be seen that the time differences are negative
for all methods with respect to the validated one; that is, the
validated algorithm's detection times lag behind those given by the
other methods. Particularly, the mean delay of the validated
algorithm is 1.1 s with respect to r.sup.2, 0.6 s with respect to
STA/LTA and 1.6 s with respect to WTMM while the mean delay of
.chi..sub.Val(t) with respect to .chi..sub.PIF(t) is 0 by
construction. As expected, the re-calculated specificity values
shifted by .tau. shown in Table 2 are higher compared to those
without shifting.
[0082] Also of note from FIG. 10 is that only 55% of seizures
detected by all methods are detected by the validated algorithm
(Val). Tau (.tau.) zero (x-axis) corresponds to the time at which
Val issues a detection. Negative .tau. values indicate "late"
detections by the validated algorithm in relation to the other
three and positive value the opposite. As discussed, r.sup.2,
STA/LTA and WTMM issue earlier detections than Val. Values of the
lags .tau. corresponding to the maximum and minimum values of each
function are presented for each graphic under the names argmax and
argmin respectively.
[0083] Except for Spe.sub.PIF.sub.--.sub.Val(.tau.) the shape of
the other specificity functions is asymmetric (FIG. 10). Negative
values of the specificity functions found for small positive mutual
shifts r are the consequence of the fact that, on average, these
time shifts correspond to periods that the Val method does not
classify as seizures, activity that the other methods do. This
alternating effect for mutually shifted seizures time intervals is
the strongest for the values of corresponding to the minimum of the
cross-covariance functions. There are also instances when other
methods do not classify some time intervals as seizures while the
validated algorithm does.
[0084] The value
max .tau. Spe PIF_Val ( .tau. ) = Spe PIF_Val ( 0 ) = 0.554
##EQU00006##
indicated in the lower right panel of FIG. 10 means that only 55.4%
of seizures recognized as such by the other methods are also
detected by the validated method, indicating that in its generic
form and by design, it is less sensitive and more specific for
seizure detection than the others.
[0085] Discussion
[0086] The three methods presented herein survey different but
inter-dependent ECoG signal properties, thus expanding the breadth
and perhaps also the depth of insight into the spectral "structure"
of epileptic seizures in a clinically relevant manner. The
Auto-Regressive model (r.sup.2), sensitive mainly to changes in
spectral shape, was chosen as the simplest and most general method,
with which to provide a statistical description of oscillations
(ECoG) that may be regarded as generated by the stochastic analogue
of a linear oscillator. The WTMM method is well suited for
estimations of changes in power variance within adjacent "short"
time windows whereas the STA/LTA uses the ratio of variances to
detect, at low computational expense, ECoG signal changes
corresponding to seizures. The validated algorithm whose
architecture is similar to that of the STA/LTA and is also
sensitive to power variances within certain frequencies (8-45 Hz)
was used as a "benchmark" since its performance has been subject to
rigorous peer-review. The r.sup.2 and STA/LTA algorithms are
implementable into implantable devices as they operate in
real-time, while the WTTM is best suited for off-line analysis
applications given its relatively high algorithmic complexity.
[0087] Whereas various performance metrics for each algorithm
pervade the Results section inevitably leading to comparisons among
them, these would be misleading and misplaced given that each
method not only operates with different parameters, but also probes
different ECoG features. The discrepancies in number and duration
of detections issued by each algorithm, which may be inherently or
operationally "irreconcilable", parallel those that possibly
characterize and define visual expert analysis. The fundamental
implication of this observation is that a unified or universal
"definition" of what cortical activity constitutes a seizure may
not be attainable (nor desirable) even through the application of
objective, advanced signal analyses methods, particularly for
seizure onset and termination segments. Algorithmic and visual
expert analysis consensus as to what grapho-elements define a
`seizure` seems to be highly dependent on when during the course of
a `seizure` a decision is made. In this context, it is noteworthy
that AIF and PIF frequently reached a value of 1, indicative of
concordance among all detection methods sometime after seizure
onset and before its termination (as determined by any of the
methods), provided the seizures reached a certain duration (20-30
s.) as it will be discussed in more detail in this issue's
accompanying article. In short, seizure onsets and terminations may
be under certain conditions universally undefinable by algorithmic
or expert visual analysis. A systematic investigation of the
differences in signal spectral properties between the
"preface"/"epilogue" and the "main body" of seizures was not
performed. It is speculated that the presence of "start-up
transients" (in a dynamical sense) and of temporo-spatial
dispersion of the ictal signal (which impacts S/N) may be most
prominent at the onset and termination of seizures. These and local
and global state-dependencies of certain signal features, account
in part for the temporal fluctuations in algorithmic detection
performance that characterize these results.
[0088] Defining seizure energy, as the product of the standard
deviation of the power of ECoG by its duration (in seconds),
reveals that the r.sup.2_ and WTMM methods identify as a continuum,
seizures that the STA/LTA and validated algorithms detect as
clusters of short seizures. The lack of correspondence between a
certain percentage of detections (11.8% for the r.sup.2 method,
2.9% for the WTMM method and 8.5% for the STA/LTA method) and the
validated algorithm may be partially attributed to brief
discontinuities in seizure activity. This phenomenon ("go-stop-go")
appears to be inherent to seizures (e.g., it is a general feature
of intermittency associated with many dynamical systems). These
discontinuities are also an "artifact" caused by the architecture
of and parameters used in each algorithm. For example, the longer
the foreground window and the higher the order statistical filter
(e.g., median vs. quartile), in the validated algorithm, the higher
the probability that "gaps" in seizure activity will go occur.
Clustering of detections is a strategy to manage dynamical or
artifactual ictal "fragmentation".
[0089] The dependencies of seizure energy on seizure duration, for
the set of icti detected by each of the methods, are depicted in
FIG. 11. A subset of seizures detected by all methods obeys a
simple law of proportionality between energy and duration, that is,
the longer the seizure, the largest its energy. However, this
relationship is far from being invariably linear, indicating the
presence of interesting scaling properties of seizure energy.
Indeed, with the exception of the validated method, the others
detect sets of seizures that are characterized by non-trivial
scaling properties and much more variability in the standard
deviation of the power of cortical activity. This can be surmised
from the slopes being different from 1 (FIG. 11) of the lower
envelops of the scatter of points in panels (c) and (d)
corresponding to the r.sup.2 and WTMM methods, and to the nonlinear
dark crescent seen in panel (b) corresponding to the STA/LTA
method. The seizures detected by the validated algorithm have the
smallest dispersion in the energy-duration relation. As expected,
the differences in seizure onset and termination times are
reflected in the energy-duration distributions; the dispersion of
standard deviations varies widely among the different methods and
non-linearities are present in certain distributions (e.g., FIG.
11b).
[0090] The conditional probabilities of durations (FIG. 12a) and of
the logarithm of energy of seizures (FIG. 12b) provide additional
support that their properties are partly a function of the method
used for their detection. The validated and STA/LTA algorithms
yield similar durations but different from those of the WTMM and
r.sup.2 methods, which are analogous to each other (FIG. 12a). The
distributions of the logarithm of seizure energies as identified by
each of the methods (FIG. 12b) reveals additional discrepancies as
evidenced by the much narrower and shorter "tail" distribution of
the validated algorithm compared to the others.
[0091] To conclude, each of the investigated methods is "sensitive"
to different seizure properties or features and may be regarded as
providing complementary dynamical and clinical relevant knowledge
with translational value. The AIF and PIF may be viewed as a first
attempt towards a more nuanced definition (probabilistic) of
seizures with operational value. That concordance levels between
methods fluctuates as a function of seizure duration, commonly
reaching its highest possible value (AIF=PIF=1) sometime (20-30 s.)
after onset, insinuate a decline in signal complexity or in its
entropy, as feature homogeneity transitorily prevails over
heterogeneity.
[0092] An embodiment of a medical device adaptable for use in
implementing some aspects of embodiments of the present invention
is provided in FIG. 1. As shown in FIG. 1, a system may involve a
medical device system that senses body signals of the patient--such
as brain or cardio-vascular activity--and analyzes those signals to
identify one or more aspects of the signal that may identify the
occurrence of a seizure. The signal may be processed to extract
(e.g., mathematically by an algorithm that computes certain values
from the raw or partially processed signal) features that may be
used to identify a seizure when compared to the inter-ictal state.
As shown in the right side of FIG. 1, the features may also be
graphically displayed either in real time or subsequent to the
event to enable visual confirmation of the seizure event and gain
additional insight into the seizure (e.g., by identifying a seizure
metric associated with the seizure).
[0093] Turning now to FIG. 2, a block diagram depiction of a
medical device 200 is provided, in accordance with one illustrative
embodiment of the present invention. In some embodiments, the
medical device 200 may be implantable (such as an implantable
electrical signal generator), while in other embodiments the
medical device 200 may be completely external to the body of the
patient.
[0094] The medical device 200 may comprise a controller 210 capable
of controlling various aspects of the operation of the medical
device 200. The controller 210 is capable of receiving internal
data or external data, and in one embodiment, is capable of causing
a therapy unit 235 to generate and deliver an electrical signal, a
drug, thermal energy, a cognitive task, or two or more thereof to
one or more target tissues of the patient's body for treating a
medical condition. For example, the controller 210 may receive
manual instructions from an operator externally, or may cause an
electrical signal to be generated and delivered based on internal
calculations and programming. In other embodiments, the medical
device 200 does not comprise a stimulation unit. In either
embodiment, the controller 210 is capable of affecting
substantially all functions of the medical device 200.
[0095] The controller 210 may comprise various components, such as
a processor 215, a memory 217, etc. The processor 215 may comprise
one or more microcontrollers, microprocessors, etc., capable of
performing various executions of software components. The memory
217 may comprise various memory portions where a number of types of
data (e.g., internal data, external data instructions, software
codes, status data, diagnostic data, etc.) may be stored. The
memory 217 may comprise one or more of random access memory (RAM),
dynamic random access memory (DRAM), electrically erasable
programmable read-only memory (EEPROM), flash memory, etc.
[0096] The medical device 200 may also comprise a power supply 230.
The power supply 230 may comprise a battery, voltage regulators,
capacitors, etc., to provide power for the operation of the medical
device 200, including delivering the therapeutic electrical signal.
The power supply 230 comprises a power source that in some
embodiments may be rechargeable. In other embodiments, a
non-rechargeable power source may be used. The power supply 230
provides power for the operation of the medical device 200,
including electronic operations and the electrical signal
generation and delivery functions. The power supply 230 may
comprise a lithium/thionyl chloride cell or a lithium/carbon
monofluoride (LiCFx) cell if the medical device 200 is implantable,
or may comprise conventional watch or 9V batteries for external
(i.e., non-implantable) embodiments. Other battery types known in
the art of medical devices may also be used.
[0097] The medical device 200 may also comprise a communication
unit 260 capable of facilitating communications between the medical
device 200 and various devices. In particular, the communication
unit 260 is capable of providing transmission and reception of
electronic signals to and from a monitoring unit 270, such as a
handheld computer or PDA that can communicate with the medical
device 200 wirelessly or by cable. The communication unit 260 may
include hardware, software, firmware, or any combination
thereof.
[0098] The medical device 200 may also comprise one or more
sensor(s) 212 coupled via sensor lead(s) 211 to the medical device
200. The sensor(s) 212 are capable of receiving signals related to
a physiological parameter, such as the patient's heart beat, blood
pressure, and/or temperature, and delivering the signals to the
medical device 200. The sensor 212 may also be capable of detecting
kinetic signal associated with a patient's motor activity. The
sensor 212, in one embodiment, may be an accelerometer. The sensor
212, in another embodiment, may be an inclinometer. In another
embodiment, the sensor 212 may be an actigraph. The sensor(s) 212
may be implanted, or in other embodiments, the sensor(s) 212 are
external structures that may be placed on the patient's skin, such
as over the patient's heart or elsewhere on the patient's torso,
limbs or head. The sensor 212, in one embodiment is a multimodal
signal sensor capable of detecting various autonomic and neurologic
signals, including kinetic/motor signals, metabolic signals,
endocrine signals, stress markers signals associated with the
patient's seizures.
[0099] In one embodiment, the medical device 200 may comprise a
body data collection module 275 that is capable of collecting body
data, e.g., cardiac data comprising fiducial time markers of each
of a plurality of heart beats or arterial or venous pulsations;
brain electrical or chemical signals; kinetic signals indicative of
the patient's motor activity and/or cognitive functions (e.g.,
complex reaction time responses, memory, language); endocrine
signals; body stress marker signals; body integrity/physical
fitness signals; etc. More information about such signals and their
detection can be found in U.S. patent application Ser. Nos.
12/896,525; 13/098,262; and 13/288,886; which are hereby
incorporated by reference in their entirety.
[0100] The body data collection module 275 may also process or
condition the body signal data. The body signal data may be
provided by the sensor(s) 212. The body data collection module 275
may be capable of performing any necessary or suitable amplifying,
filtering, and performing analog-to-digital (A/D) conversions to
prepare the signals for downstream processing. The body data
collection module 265, in one embodiment, may comprise software
module(s) that are capable of performing various interface
functions, filtering functions, etc. In another embodiment, the
body data collection module 275 may comprise hardware circuitry
that is capable of performing these functions. In yet another
embodiment, the body data collection module 275 may comprise
hardware, firmware, software and/or any combination thereof.
[0101] The body data collection module 275 is capable of collecting
body data and providing the collected body data to a seizure
onset/termination unit 280.
[0102] The seizure onset/termination unit 280 is capable of
detecting an onset and/or a termination of an epileptic event based
upon at least one body signal provided by body data collection
module 275. The seizure onset/termination unit 280 can implement
one or more algorithms using the autonomic data and neurologic data
in any particular order, weighting, etc. The seizure
onset/termination unit 280 may comprise software module(s) that are
capable of performing various interface functions, filtering
functions, etc. In another embodiment, the seizure
onset/termination unit 280 may comprise hardware circuitry that is
capable of performing these functions. In yet another embodiment,
the seizure onset/termination unit 280 may comprise hardware,
firmware, software and/or any combination thereof.
[0103] In addition to components of the medical device 200
described above, a medical device system may comprise a storage
unit to store an indication of at least one of seizure or an
increased risk of a seizure. The storage unit may be the memory 217
of the medical device 200, another storage unit of the medical
device 200, or an external database, such as a local database unit
255 or a remote database unit 250. The medical device 200 may
communicate the indication via the communications unit 260.
Alternatively or in addition to an external database, the medical
device 200 may be adapted to communicate the indication to at least
one of a patient, a caregiver, or a healthcare provider.
[0104] In various embodiments, one or more of the units or modules
described above may be located in a monitoring unit 270 or a remote
device 292, with communications between that unit or module and a
unit or module located in the medical device 200 taking place via
communication unit 260. For example, in one embodiment, one or more
of the body data collection module 275 or the seizure
onset/termination unit 280 may be external to the medical device
200, e.g., in a monitoring unit 270. Locating one or more of the
body data collection module 275 or the seizure onset/termination
unit 280 outside the medical device 200 may be advantageous if the
calculation(s) is/are computationally intensive, in order to reduce
energy expenditure and heat generation in the medical device 200 or
to expedite calculation.
[0105] The monitoring unit 270 may be a device that is capable of
transmitting and receiving data to and from the medical device 200.
In one embodiment, the monitoring unit 270 is a computer system
capable of executing a data-acquisition program. The monitoring
unit 270 may be controlled by a healthcare provider, such as a
physician, at a base station in, for example, a doctor's office. In
alternative embodiments, the monitoring unit 270 may be controlled
by a patient in a system providing less interactive communication
with the medical device 200 than another monitoring unit 270
controlled by a healthcare provider. Whether controlled by the
patient or by a healthcare provider, the monitoring unit 270 may be
a computer, preferably a handheld computer or PDA, but may
alternatively comprise any other device that is capable of
electronic communications and programming, e.g., hand-held computer
system, a PC computer system, a laptop computer system, a server, a
personal digital assistant (PDA), an Apple-based computer system, a
cellular telephone, etc. The monitoring unit 270 may download
various parameters and program software into the medical device 200
for programming the operation of the medical device, and may also
receive and upload various status conditions and other data from
the medical device 200. Communications between the monitoring unit
270 and the communication unit 260 in the medical device 200 may
occur via a wireless or other type of communication, represented
generally by line 277 in FIG. 2. This may occur using, e.g., a wand
to communicate by RF energy with an implantable signal generator
110. Alternatively, the wand may be omitted in some systems, e.g.,
systems in which the MD 200 is non-implantable, or implantable
systems in which monitoring unit 270 and MD 200 operate in the MICS
bandwidths.
[0106] In one embodiment, the monitoring unit 270 may comprise a
local database unit 255. Optionally or alternatively, the
monitoring unit 270 may also be coupled to a database unit 250,
which may be separate from monitoring unit 270 (e.g., a centralized
database wirelessly linked to a handheld monitoring unit 270). The
database unit 250 and/or the local database unit 255 are capable of
storing various patient data. These data may comprise patient
parameter data acquired from a patient's body, therapy parameter
data, seizure severity data, and/or therapeutic efficacy data. The
database unit 250 and/or the local database unit 255 may comprise
data for a plurality of patients, and may be organized and stored
in a variety of manners, such as in date format, severity of
disease format, etc. The database unit 250 and/or the local
database unit 255 may be relational databases in one embodiment. A
physician may perform various patient management functions (e.g.,
programming detection parameters for a responsive therapy and/or
setting references for one or more detection parameters) using the
monitoring unit 270, which may include obtaining and/or analyzing
data from the medical device 200 and/or data from the database unit
250 and/or the local database unit 255. The database unit 250
and/or the local database unit 255 may store various patient
data.
[0107] One or more of the blocks illustrated in the block diagram
of the medical device 200 in FIG. 2 may comprise hardware units,
software units, firmware units, or any combination thereof.
Additionally, one or more blocks illustrated in FIG. 2 may be
combined with other blocks, which may represent circuit hardware
units, software algorithms, etc. Additionally, any number of the
circuitry or software units associated with the various blocks
illustrated in FIG. 2 may be combined into a programmable device,
such as a field programmable gate array, an ASIC device, etc.
[0108] Turning now to FIG. 3, a block diagram depiction of an
exemplary implementation of the body data collection module 275 is
shown. The body data collection module 275 may include a body data
memory 350 (which may be independent of memory 117 or part of it)
for storing and/or buffering data. The body data memory 350 may be
adapted to store body data for logging or reporting and/or for
future body data processing and/or statistical analyses. Body data
collection module 275 may also include one or more body data
interfaces 310 for input/output (I/O) communications between the
body data collection module 275 and sensors 112.
[0109] In the embodiments of FIG. 3, sensors 112 may be provided as
any of various body data units/modules (e.g., autonomic data
acquisition unit 360, neurological data acquisition unit 370,
endocrine data acquisition unit 373, metabolic data acquisition
unit 374, tissue stress marker data acquisition unit 375, and
physical fitness/integrity determination unit 376) via connection
380. Connection 380 may be a wired connection, a wireless
connection, or a combination of the two. Connection 380 may be a
bus-like implementation or may include an individual connection
(not shown) for all or some of the body data units.
[0110] In one embodiment, the autonomic data acquisition unit 360
may include a cardiac data acquisition unit 361 adapted to acquire
a phonocardiogram (PKG), EKG, echocardiography, apexcardiography
and/or the like, a blood pressure acquisition unit 363, a
respiration acquisition unit 364, a blood gases acquisition unit
365, and/or the like. In one embodiment, the neurologic data
acquisition unit 370 may contain a kinetic unit 366 that may
comprise an accelerometer unit 367, an inclinometer unit 368,
and/or the like; the neurologic data acquisition unit 370 may also
contain a responsiveness/awareness unit 369 that may be used to
determine a patient's responsiveness to testing/stimuli and/or a
patient's awareness of their surroundings. Other units (not shown)
that may be used to acquire body data include, but are not limited
to, tools for chemical assays, optical measuring tools, pressure
measuring tools, and temperature measuring tools. Body data
collection module 275 may collect additional data not listed
herein, that would become apparent to one of skill in the art
having the benefit of this disclosure.
[0111] The body data acquisition units ([360-370], [373-377]) may
be adapted to collect, acquire, receive/transmit heart beat data,
EKG, PKG, echocardiogram, apexcardiogram, blood pressure,
respirations, blood gases, body acceleration data, body inclination
data, EEG/ECoG, quality of life data, physical fitness data, and/or
the like.
[0112] The body data interface(s) 310 may include various
amplifier(s) 320, one or more A/D converters 330 and/or one or more
buffers 340 or other memory (not shown). In one embodiment, the
amplifier(s) 320 may be adapted to boost and condition incoming
and/or outgoing signal strengths for signals such as those to/from
any of the body data acquisition units/modules (e.g., ([360-370],
[373-377])) or signals to/from other units/modules of the MD 200.
The A/D converter(s) 330 may be adapted to convert analog input
signals from the body data unit(s)/module(s) into a digital signal
format for processing by controller 210 (and/or processor 215). A
converted signal may also be stored in a buffer(s) 340, a body data
memory 350, or some other memory internal to the MD 200 (e.g.,
memory 117, FIG. 1) or external to the MD 200 (e.g., monitoring
unit 170, local database unit 155, database unit 150, and remote
device 192). The buffer(s) 340 may be adapted to buffer and/or
store signals received or transmitted by the body data collection
module 275.
[0113] As an illustrative example, in one embodiment, data related
to a patient's respiration may be acquired by respiration unit 364
and sent to MD 200. The body data collection module 275 may receive
the respiration data using body data interface(s) 310. As the data
is received by the body data interface(s) 310, it may be
amplified/conditioned by amplifier(s) 320 and then converted by A/D
converter(s) into a digital form. The digital signal may be
buffered by a buffer(s) 340 before the data signal is transmitted
to other components of the body data collection module 275 (e.g.,
body data memory 350) or other components of the MD 200 (e.g.,
controller 110, processor 115, memory 117, communication unit 160,
or the like). Body data in analog form may be also used in one or
more embodiments.
[0114] Body data collection module 275 may use body data from
memory 350 and/or interface 310 to calculate one or more body
indices. A wide variety of body indices may be determined,
including a variety of autonomic indices such as heart rate, blood
pressure, respiration rate, blood oxygen saturation, neurological
indices such as maximum acceleration, patient position (e.g.,
standing or sitting), and other indices derived from body data
acquisition units 360, 370, 373, 374, 375, 376, 377, etc.
[0115] Turning now to FIG. 4, an MD 200 (as described in FIG. 3) is
provided, in accordance with one illustrative embodiment of the
present invention. FIG. 4 depicts the body data acquisition units
similar to those shown in FIG. 3, in accordance with another
embodiment, wherein these units are included within the MD 200,
rather being externally coupled to the MD 200, as shown in FIG. 3.
In accordance with various embodiments, any number and type of body
data acquisition units may be included within the MD 200, as shown
in FIG. 4, while other body data units may be externally coupled,
as shown in FIG. 3. The body data acquisition units may be coupled
to the body data collection module 275 in a fashion similar to that
described above with respect to FIG. 3, or in any number of
different manners used in coupling intra-medical device modules and
units The manner by which the body data acquisition units may be
coupled to the body data collection module 275 is not essential to,
and does not limit, embodiments of the instant invention as would
be understood by one of skill in the art having the benefit of this
disclosure. Embodiments of the MD depicted in FIG. 4 may be fully
implantable or may be adapted to be provided in a system that is
external to the patient's body.
[0116] A time series body signal collected by the body data
collection module 275 may comprise at least one of a measurement of
the patient's heart rate, a measurement of the patient's kinetic
activity, a measurement of the patient's brain electrical activity,
a measurement of the patient's oxygen consumption, a measurement of
the patient's work, a measurement of an endocrine activity of the
patient, a measurement of a metabolic activity of the patient, a
measurement of an autonomic activity of the patient, a measurement
of a cognitive activity of the patient, or a measurement of a
tissue stress marker of the patient.
[0117] Turning to FIG. 5, the seizure onset/termination unit 280
depicted in FIG. 2 is shown in greater detail. The seizure
onset/termination unit 280 may comprise a body signal processing
unit 510 adapted to process collected body data from the body data
collection module 275. For example, the seizure onset/termination
unit 280 may be adapted to receive a time series of collected body
data.
[0118] The seizure onset/termination unit 280 may also comprise a
plurality of algorithm units 521, 522, 523, and optionally others
not shown. Each algorithm unit 521, etc. may apply an algorithm to
body signal data to determine an occurrence of a seizure, as
described herein. At least two algorithms may be applied to at
least one body signal. In one embodiment, detection algorithms
operating in real-time may be used, and in another embodiment,
real-time and/or off-line algorithms (not operating in real-time
due to window length or computational demands) may be used to
confirm in a probabilistic way detections made in real-time. The
PMSA may thus be applied to body signal time series in: 1.
Real-time/on-line for detection, therapy delivery, and warning
purposes, and 2. Off-line for assessment of algorithm/PMSA
performance and/or tracking of disease status (e.g., progression or
regression) through quantification of event frequency, severity,
inter-event intervals, or time spent in seizure, etc. More detail
on tracking disease status and the like can be found in U.S. patent
application Ser. Nos. 12/816,348 and 12/816,357, both filed Jun.
15, 2010, and both hereby incorporated herein by reference.
[0119] Body signals may belong to the same class (e.g., cardiac) or
to different classes (e.g., cardiac, neurologic, endocrine) and
each signal class has subclasses; for example, cardiac signals may
be electrical (EKG), acoustic (PKG, Echocardiography), or thermal,
and neurological signals may be chemical or optical, among others.
Real-time and/or off-line analyses may be performed on at least one
biological signal class (e.g., cardiac) or on to at least two
(e.g., cardiac, neurologic and metabolic). Furthermore, within each
class one or more sub-classes may be analyzed with at least one
algorithm; in the case of cardiac signals, EKG, PKG, and pulse
pressure may be chosen for event detection and in that of
neurological signals, the analyzed subclasses may be ECoG,
responsiveness/awareness or kinetic. The number of possible signal
analyses permutations of detection algorithms, signal classes and
signal subclasses for estimation of a PMSA is
n.sub.algorithms.times.n.sub.classes.times.n.sub.sub-classes.
[0120] Multiple average indicator functions (AIFs) may be derived
for computing a PMSA:
AIF.sub.uni-class(t)=1/N.SIGMA..chi..sub.algorithms(1 . . .
n)(t)
AIF.sub.uni-class/multi-subclass(t)=1/N.SIGMA..chi..sub.algorithms(1
. . . n)(t)
AIF.sub.multi-class/uni-sub-class(t)=1/N.SIGMA..chi..sub.algorithms(1
. . . n)(t)
AIF.sub.multi-class-multi-subclass(1 . . .
n)(t)=1/N.SIGMA..chi..sub.algorithms(1 . . . n)(t)
[0121] Similarly, multiple product indicator functions may be
derived for computing a PMSA:
PIF.sub.uni-class(t)=.chi..sub.algorithm1(t).chi..sub.algorithm2(t).chi.-
.sub.algorithm3(t).chi..sub.algorithmn(t)
PIF.sub.uni-class/multi-subclass(t)=.chi..sub.algorithm1(t).chi..sub.alg-
orithm2(t).chi..sub.algorithm3(t).chi..sub.algorithmn(t)
PIF.sub.multi-class/uni-sub-class(t)=.chi..sub.algorithm1(t).chi..sub.al-
gorithm2(t).chi..sub.algorithm3(t).chi..sub.algorithmn(t)
PIF.sub.multi-class-multi-subclass(t)=.chi..sub.algorithm1(t).chi..sub.a-
lgorithm2(t).chi..sub.algorithm3(t).chi..sub.algorithmn(t)
[0122] The seizure onset/termination unit 280 may also comprise an
indicator function (IF) unit 530. The IF unit 530 may be adapted to
determine an indicator function, such as an average indicator
function (AIF), or a product indicator function (PIF) to the
outputs of algorithm units 521, etc., which may be weighted
linearly or non-linearly. In the case of the PIF, weights are
meaningfully applied only when the all the indicator functions have
a value of 1.
[0123] The seizure onset/termination unit 280 may also comprise an
indicator-threshold comparison unit 540. The indicator-threshold
comparison unit 540 may be adapted to compare a value of an IF
output by the IF unit 530 to a detection threshold value.
[0124] A medical device or medical device system as shown in one or
more of FIGS. 2-5 may be configured to perform in whole or in part
one or more methods described below.
[0125] As shown in FIG. 15, in one aspect, the present disclosure
provides a method of detecting a seizure in a patient.
[0126] The method may comprise providing at 1510 at least first and
second seizure detection algorithms for detecting seizure activity
based upon at least one body signal or at least one class of body
signals.
[0127] The at least first and second seizure detection algorithms
may be selected from an autoregression algorithm, a wavelet
transform maximum modulus (WTMM) algorithm, or a short-term-average
to long-term-average (STALTA) algorithm, such as those described
above. Any other algorithms may be applied to a time series to
generate indicator functions to compute an AIF or a PIF.
[0128] The at least one body signal may comprise at least one of a
measurement of the patient's autonomic, neurologic, endocrine, or
metabolic activity, or a measurement of a tissue stress marker of
the patient. In various exemplary embodiments, a measurement of an
autonomic activity may be a measurement of at least one of a
cardiac signal, a respiratory signal a measurement of the patient's
oxygen consumption, a measurement of the patient's oxygen
saturation, a measurement of a skin signal or of catecholamines; a
measurement of neurologic signal may be at least one of a
measurement of a cognitive signal, a measurement of a kinetic
signal, or a measurement of a brain electrical or chemical signal;
a measurement of a metabolic signal may be at least one of a
measurement of an arterial pH, a measurement of an electrolyte
signal, or a measurement of a glucose signal; a measurement of an
endocrine signal may be at least one of a measurement of a
prolactin signal and a measurement of a cortisol signal, a
measurement of a body stress marker signal may be at least one of a
measurement of a lactic acid signal and a measurement of a prostane
signal. Pressure measurements in brain, heart, or vascular tree may
be also performed.
[0129] The method may also comprise determining at 1520 a
probabilistic measure of seizure activity (PMSA) based upon the
outputs of the at least first and second seizure detection
algorithms. In one embodiment, determining at 1520 may comprise
determining an average indicator function by averaging the outputs
of the at least first and second seizure detection algorithms. In
another embodiment, determining at 1520 may comprise determining a
product indicator function by multiplying the outputs of the at
least first and second seizure detection algorithms. In yet another
embodiment, determining at 1520 may comprise determining both the
average and product indicator functions, without weights, or
linearly or non-linearly weighted.
[0130] In one optional aspect, the method may further comprise
selecting one or more of a number of seizure detection algorithms
(i.e., whether one, two, three, four, or a greater number of
seizure detection algorithms are to be used), the at least first
and second seizure detection algorithms (i.e., which particular
seizure detection algorithms are to be used), at least one
parameter of at least one of the first and second seizure detection
algorithms, a type of the PMSA (e.g., a PIF, an AIF, or another
type of PMSA), or at least one parameter of the PMSA (e.g., a
weighting between IFs making up the PMSA; a threshold for the PMSA
value) based upon one or more of: a clinical application; a level
of safety risk associated with an activity; at least one of an age,
physical state, or mental state of the patient; a length of a
window available for warning; a degree of efficacy of therapy and
of the latency of its effect; a degree of seizure control; a degree
of circadian and ultradian fluctuations of the patient's seizure
activity; a performance of the detection method as a function of
the patient's sleep/wake cycle or vigilance level; a dependence of
the patient's seizure occurrence on at least one of a level of
consciousness, a dependence of the patient's seizure occurrence on
circadian or ultradian rhythms, a level of cognitive activity, or a
level of physical activity; the site of seizure origin; a seizure
type or class; a proclivity for the seizure to spread or to impair
cognitive or motor functions; a proclivity of the seizure to cause
falls to the ground; a time elapsed from a previous seizure; a
desired sensitivity of detection of a seizure; a desired
specificity of detection of a seizure: a desired speed of detection
of a seizure; a time elapsed since a previous seizure; a previous
seizure severity; a probability of seizure occurrence; a likelihood
of seizure occurrence; an input provided by a person (e.g., the
patient or a care-giver); or an input provided by a machine/device
(e.g., a sensor).
[0131] In one embodiment, the number of algorithms, the specific
algorithms, the parameters of the PMSA (e.g., the weighting, the
threshold), etc. may be established based at least in part on at
least one of a measurement of the patient's heart activity, a
measurement of the patient's respiratory activity, a measurement of
the patient's kinetic activity, a measurement of the patient's
brain electrical activity, a measurement of the patient's oxygen
consumption, a measurement of the patient's oxygen saturation, a
measurement of an endocrine activity of the patient, a measurement
of a metabolic activity of the patient, a measurement of an
autonomic activity of the patient, a measurement of a cognitive
activity of the patient, or a measurement of a tissue stress marker
of the patient. In certain embodiments, such a basis may be a form
of adaptation since the signal values may be different for each
signal class and for each subclass. For example, a kinetic signal
may be the first one to change in certain seizures (e.g.,
convulsions), while a heart rate signal may be the last one to
increase (e.g., in partial seizures originating outside central
autonomic brain regions; said increase manifesting only when it
becomes generalized).
[0132] The choice of multiple parameters, e.g., a body signal and
PMSA threshold value, may be performed independently of one another
based on their positive predictive value and/or information
content. For example, a person of ordinary skill in the art may
choose a body signal that gives high sensitivity and/or specificity
for seizure detection, and also choose the PMSA threshold based on
the clinical application, time of day, etc. For example, when the
patient is in bed, the PMSA threshold may be set higher than when
the patient is standing, when kinetic body signals indicative of
convulsions are used by the algorithm(s).
[0133] In various embodiments, the selected parameters may reflect
the degree of certainty of detections desired by the patient, a
caregiver, a medical professional, or two or more thereof. Such
person(s) are expected to have biases regarding their desire for
certainty of detection, and variations or differences in their
risk-proneness and/or aversion to risk. Thus, in one embodiment,
the patient, caregiver, and/or medical professional may be allowed
to change (within certain limits and for certain activities only,
if desired) the sensitivity, specificity, and/or speed of detection
of the algorithms.
[0134] Parameters of the PMSA include, but are not limited to, the
AIF or PIF value at which a seizure onset and/or termination is
declared, or the weights assigned to the various algorithms used in
calculating an AIF or PIF. Thus, in one aspect, the method may
comprise comparing the PMSA to a PMSA threshold value, and
detecting at 1530 a seizure event when the PMSA reaches or exceeds
the PMSA threshold, imposing, when desirable, duration constraints.
In one embodiment, wherein n algorithms are used in an AIF without
weighting, the PMSA threshold may be (n-1)/n. For example, if four
algorithms are used in an AIF, without weights, then the PMSA
threshold may be 0.75, indicating three of the four algorithms
agree a seizure event has occurred or is occurring. In a further
embodiment, the method may additionally comprise adaptively setting
the PMSA threshold value. For example, the threshold value may be
adaptively set based on one or more of time of day or risk of
seizure, among other considerations. The adaption may be performed
manually (e.g., by the patient or a caregiver) or automatically
(e.g., by a controller 210 operating on data stored in memory 217,
monitoring unit 270, database unit 250, etc. and suitably
programmed to adapt the threshold).
[0135] Upon a determination at 1520 of the PMSA, one or more
optional actions may be performed. For example, the method may
further comprise delivering at 1540 a therapy for the seizure at a
particular time, wherein at least one of the therapy, the
particular time, or both is based upon the PMSA value. In a further
embodiment, the method may further comprise determining at 1550 at
least one of an efficacy of the therapy or an occurrence of at
least one side effect of the therapy. For another example, the
method may further comprise issuing at 1560 a warning for the
seizure, wherein the warning is based upon the PMSA value, the type
of warning being commensurate with said value. For another example,
the method may further comprise logging at 1570 one or more values
relating to the seizure severity (e.g., a duration of the seizure;
an intensity of the seizure; a degree of spread of the seizure in
the brain of the patient; or a fraction of time spent in the
seizure over a moving time window), the detection and termination
times and date, the type of therapy and its length, efficacy and
the side effects if any, the warning type and duration, or other
information of interest to the person of ordinary skill in the art,
as well as information about the physical integrity of the
patient.
[0136] In one embodiment, at least one of the delivered therapy or
the issued warning may be based at least in part on the type of
activity engaged in by the patient at the time of seizure onset,
the seizure type, the seizure severity, or the time elapsed from
the last seizure.
[0137] Turning to FIG. 16, a method of detecting a seizure in
accordance with one embodiment of the present disclosure is
depicted. In the depicted embodiment, a first body signal may be
received at 1610 by a first sensor and a second body signal by a
second sensor. Exemplary body signals are set forth above. The
first and second sensors may both be sensors of one type (e.g., two
electrodes configured to receive brain electrical signals), wherein
the at least two sensors are sited at separate locations on or in a
patient's body, or may be of multiple types (e.g., electrical,
chemical, optical, pressure sensors) configured to detect a
patient's body signals located on or in a patient's body. The first
and second body signals may be of the same class or subclass, or
may be of different classes. In one particular embodiment, the
first and second body signals may be from two different subclasses
from at least one signal class.
[0138] From a signal, a PMSA may then be determined at 1620. In
other words, seizure detections each based on a data stream
received by one of the at least two sensors can be used in a PMSA,
comparably to the way multiple algorithms operating on the same
data stream were used in a PMSA in embodiments discussed above.
That is, in view of the number of combinations of
n.sub.algorithms.times.n.sub.classes.times.n.sub.sub-classes making
up a PMSA, in one embodiment, n.sub.algorithms may be 1 and the
total of (n.sub.classes+n.sub.subclasses) may be 2 or more. In
other embodiments, of course, n.sub.algorithms may be 2 or more and
the total of (n.sub.classes+n.sub.subclasses) may be 1 or more.
[0139] In one embodiment, only one sensor placed on the organ or
site of interest and a reference one may be used.
[0140] Turning to FIG. 17, a method of detecting a seizure in a
patient is depicted. The method of FIG. 17 contains numerous
elements in common with FIG. 15. Those elements have been described
above and need not be described further.
[0141] In the method of FIG. 17, a wavelet transform maximum
modulus-stepwise approximation (WTMM-Sp) algorithm for detecting
seizure activity based upon at least one body signal may be
provided at 1710. As described earlier, a WTMM-Sp algorithm can be
used to generate a plurality of outputs indicative of detection of
a seizure. The method may thus comprise determining at 1520 a
probabilistic measure of seizure activity (PMSA) based upon a
plurality of outputs of the WTMM-Sp algorithm.
[0142] Turning to FIG. 18, another method in accordance with one
illustrative embodiment of the present disclosure is depicted.
[0143] The method may comprise receiving at 1810 at least one body
signal. Exemplary body signals have been described herein.
[0144] The method may comprise using at 1820 a first algorithm to
determine at least one of a seizure onset, seizure termination, or
an occurrence of a seizure from the at least one body signal, and
to assign a first value based upon a determination that at least
one of the seizure onset, seizure termination, or the occurrence of
a seizure has occurred. The first algorithm may be an
autoregression algorithm, a WTMM algorithm, or a STA/LTA algorithm,
as described herein.
[0145] The method may comprise using at 1830 a second algorithm to
determine at least one of the seizure onset, seizure termination,
or the occurrence of a seizure from the at least one body signal,
and to assign a second value based upon a determination that at
least one of the seizure onset, seizure termination, or an
occurrence of a seizure has occurred. The second algorithm may be
an autoregression algorithm, a WTMM algorithm, or a STA/LTA
algorithm, as described herein.
[0146] Optionally, the method may further comprise using at 1835 a
third algorithm to determine at least one of the seizure onset,
seizure termination, or the occurrence of a seizure from the at
least one body signal, and to assign a third value based upon a
determination that at least one of the seizure onset, seizure
termination, or the occurrence of a seizure has occurred. The third
algorithm may be an autoregression algorithm, a WTMM algorithm, or
a STA/LTA algorithm, as described herein.
[0147] Additional algorithms may be used to assign additional
values, if desired.
[0148] Based on the assigned values, the method may comprise
determining at 1840 an average indicator function (AIF) value or a
product indicator function (PIF) value. AIFs and PIFs have been
described herein.
[0149] The method may comprise comparing at 1850 the AIF value or
the PIF value to a detection threshold, said threshold being fixed,
adaptable, or self-adaptive.
[0150] The method may comprise determining at 1860 that a seizure
has occurred based upon a determination that the AIF value is above
the threshold or PIF value equals one. For example, if an AIF is
determined at 1840, the AIF threshold value may be to be e.g. 0.66
(thus resulting in a determination at 1860 by agreement between
2/3, or a greater fraction of algorithms) or 0.75 (thus resulting
in a determination at 1860 by agreement between 3/4 or a greater
fraction of algorithms).
[0151] Optionally, the method may further comprise other actions.
In one embodiment, the method may further comprise determining at
1865 at least one of the duration, the intensity, or the extent of
spread of the seizure.
[0152] Alternatively or in addition, the method may further
comprise determining at 1870 at least one of a timing of delivery
of therapy, a rate of delivery of a therapy, a therapy type, a
timing of sending a warning, a warning type, a warning duration, or
an efficacy index based upon a timing of the average indicator
function value.
[0153] A non-transitory computer readable program storage unit
encoded with instructions that, when executed by a computer, may
perform any method described herein.
[0154] In one or more of the methods described above, an activity,
such as walking, swimming, driving, etc., may be allowed,
precluded, or terminated, a warning may be issued or not issued, or
a therapy may be delivered or not delivered, based on the PMSA
value.
[0155] At least one algorithm may be selected based on at least one
of specificity, sensitivity, positive predictive value (PPV), or
speed of detection.
[0156] PMSA may be used to determine, in whole or in part, at least
one of a probability of delivering a warning, a probability of
delivering a therapy, a type of a warning, a type of a treatment,
or a site or sites of delivery of a treatment.
[0157] PMSA may be weighted by latency to at least one of loss of
responsiveness, seizure severity (SS), seizure frequency (SF), time
between seizures (TBS), or efficacy of a therapy. This weighting
may allow adapting a probabilistic seizure detection to the
patient's seizure type, frequency, and/or severity.
[0158] The signals analyzed by the algorithms used in determining
the PMSA may be collected simultaneously or substantially
simultaneously (to yield what may be termed an "instantaneous
PSMA") or may be collected over a non-simultaneous period of time
(to yield what may be termed as a "staggered PMSA").
[0159] The algorithm(s) selected for use in determining the PSMA
may be ranked in hierarchy based on one or more of speed of
detection, specificity, sensitivity, or other parameters.
[0160] An "efficacy index" (which may be termed "EI") may be used
herein to refer to any quantification of an efficacious result of a
therapy. In one example, if a patient typically presents with an
increase in heart rate from a resting rate of 80 beats per minute
(BPM) to a peak ictal heart rate of 160 BPM, and upon administering
a therapy to the patient, the patient's peak ictal heart rate is
110 BPM, this result may be quantified as an efficacy index in a
number of ways. For example, an efficacy index may be calculated as
(non-therapy peak ictal heart rate) minus (peak ictal heart rate
after therapy), e.g., in this example, 50. For another example, an
efficacy index may be calculated as (reduction from peak ictal
heart rate brought about by therapy) divided by (increase from
resting rate to peak ictal heart rate in the absence of therapy),
e.g., in this example, 50/80, or 0.625. Other ways of calculating
an efficacy index may be used.
[0161] A probabilistic efficacy index (PEI) may also be computed.
In some embodiments, the probabilistic efficacy index may be given
by the difference in the value of the PMSA of un-treated compared
to that of treated seizures (PMSA.sub.un-treated-PMSA.sub.treated),
or by the differences of PMSA value between a first and a second
therapy (PMSA.sub.therapy1-PMSA.sub.therapy2). For example, if a
PMSA value calculated using an AIF based on four algorithms is 1
(meaning all algorithms are in agreement) 60 sec. after issuance of
a detection in untreated seizures, and 0.25 (meaning only one of
the four algorithms identifies a seizure) two seconds after
termination of a 1 sec. therapy, triggered 2 sec. after detection
(e.g., at 5 sec after issuance of a detection in the treated
seizure), the PEI may be calculated as the difference in PMSA
values (in this example, 1-0.25=0.75) multiplied by the difference
in time after detection at which the PMSA values are calculated (in
this example, 60 sec-5 sec=55 sec). In other words, in this
example, the PEI is 0.75.times.55 (sec)=42. The PEI of this
example, or one calculated in a similar manner, may be considered a
time-based PEI. In another embodiment, the PEI may be independent
of time and reflect only the PMSA differences between un-treated
and treated seizures. For example, a time-independent PEI from the
foregoing example may be calculated as the difference in PMSA
values (e.g., 1-0.25=0.75).
[0162] Further, the efficacy index EI may be added to or multiplied
by the PEI to generate a comprehensive efficacy index CEI:
CEI=EI+PEI or CEI=EI.times.PEI.
[0163] Whether or not to use PIF or AIF may be based on at least
one of a clinical application of the output; a level of safety risk
associated with an activity; an age, physical and mental state of
the patient; a history of degree of concordance or discordance of
outputs of seizure detection algorithms applied to the signal; a
length of a window available for warning before the patient becomes
impaired; a degree of efficacy of therapy and of the latency of its
effect; a degree of seizure control; or a degree of circadian and
ultradian fluctuations of PMSA and of its dependence on a level of
consciousness, a level of cognitive activity and a level of
physical activity. The number of algorithms and particular
algorithms used in the PIF or AIF may each be based on at least one
of a clinical application of the output; a level of safety risk
associated with an activity; an age, physical and mental state of
the patient; a history of degree of concordance or discordance of
outputs of seizure detection algorithms applied to the signal; a
length of a window available for warning; a degree of efficacy of
therapy and of its latency; a degree of seizure control; or a
degree of circadian and ultradian fluctuations of PMSA and of its
dependence on a level of consciousness, a level of cognitive
activity and a level of physical activity.
[0164] PIF may have a faster computation than AIF. Mathematically,
if one of the inputs to a PIF is zero, the PIF must be zero.
Therefore, a calculation of a PIF can be stopped when the first
zero is encountered among the inputs to the function.
[0165] All of the methods and apparatuses disclosed and claimed
herein may be made and executed without undue experimentation in
light of the present disclosure. While the methods and apparatus of
this disclosure have been described in terms of particular
embodiments, it will be apparent to those skilled in the art that
variations may be applied to the methods and apparatus and in the
steps, or in the sequence of steps, of the method described herein
without departing from the concept, spirit, and scope of the
disclosure, as defined by the appended claims. It should be
especially apparent that the principles of the disclosure may be
applied to selected cranial nerves other than, or in addition to,
the vagus nerve to achieve particular results in treating patients
having epilepsy, depression, or other medical conditions.
[0166] The particular embodiments disclosed above are illustrative
only as the disclosure may be modified and practiced in different
but equivalent manners apparent to those skilled in the art having
the benefit of the teachings herein. Furthermore, no limitations
are intended to the details of construction or design herein shown
other than as described in the claims below. It is, therefore,
evident that the particular embodiments disclosed above may be
altered or modified and all such variations are considered within
the scope and spirit of the disclosure. Accordingly, the protection
sought herein is as set forth in the claims below.
* * * * *